Course number: | MATH-UA 234 |

Semester: | Spring 2014 |

Time & Location: | Tues & Thurs, 3:30pm - 4:45pm in WWH 512 |

Instructor: | Mike O'Neil (oneil@cims.nyu.edu) |

Office hours: | Tues 10:00am - 11:00am & Wed 4:00pm - 5:00pm in WWH 1105A |

Recitation: | Fri 2:00pm - 3:15pm in WWH 512 |

Teaching assistant: | Sinziana Datcu (datcu@cims.nyu.edu) |

This course is intended as a thorough mathematical introduction to the theory of statistics, intended to be taken after sufficiency in probability is obtained at the level of Math 233: Theory of Probability. Topics covered in this class will include: sampling theory, hypothesis testing, point (parameter) estimation, regression, tests of significance, likelihood methods, and Bayesian statistics. Topics in computational statistics will be covered using Python and Pandas.

Download a copy of the syllabus here.

The course text is *All of Statistics* by Larry Wasserman. It can be accessed online through
Springer from NYU connected
computers at: http://www.springer.com/statistics/statistical+theory+and+methods/book/978-0-387-40272-7

Several other sources might be useful for studying and reference:

- Freedman, Pisani, and Purves,
*Statistics*. - Rice,
*Mathematical Statistics and Data Analysis*. - McKinney,
*Python for Data Analysis* *Schaum's Outline of Statistics**Schaum's Outline of Probability and Statistics*- Durrett,
*The Essentials of Probability*

The following Python tutorials might be useful:

Important information for the course will appear below as necessary.

- The midterm is scheduled for in-class on Thursday, March 13th.
- The final is scheduled by NYU and is Thursday, May 15th, 4:00pm-5:50pm in room WWH 312.

Below is a list of homework assignments along with the due date. Remember that each assignment is due at the beginning of class on the due date.

- Install (or get access to) Python and Pandas. I recommend installing Anaconda as a one-stop shopping experience to getting Python up and running on your machine: Watch the 10 minute intro video to Pandas here: Experiment with the above Python tutorials through Google or NYU Lynda.

- (Due 2/14/14) Problems from All of Statistics:
- Section 1.10: 15, 19
- Section 2.14: 4, 9, 14, 16, 20, 21

- (Due 2/21/14) Problems from All of Statistics:
- Section 2.14: 17
- Section 3.8: 1, 3, 4, 6, 7

- (Due 2/28/14) Problems from All of Statistics:
- Section 3.8: 8, 10, 13, 15, 16

- (Due 3/7/14) Problems from All of Statistics:
- Section 3.8: 17, 21, 22
- Section 5.8: 2, 4

- (Due 3/14/14) Problems from All of Statistics:
- Section 2.14: 8
- Section 3.8: 2
- Section 5.8: 6, 14
- Section 6.6: 1, 3
- Section 9.14: 1

- (Due 4/4/14) Problems from All of Statistics:
- Section 9.14: 2 (ignore part d), 4, 5, 6 (ignore part d)

- (Due 4/11/14) Click here for the PDF
- (Due 4/18/14) Problems from All of Statistics:
- Section 10.11: 2, 5, 6, 8, 13

- (Due 4/25/14) Problems from All of Statistics:
- Section 11.12: 1, 3, 5, 6

- (Due 5/2/14) Problems from All of Statistics:
- Section 13.10: 1, 2, 4, 5

- (Due 5/9/14) Problems from All of Statistics:
- Section 20.7: 1, 4, 8